A large number of transcription factors have been impliacted in tumorigenesis, yet little is known about their genomic binding sites under normal and pathological conditions. In order to understand the molecular mechanisms whereby abnormal functions of these factors lead to cancer, we propose to develop a genome wide location analysis (GWLA) technique that allows the rapid identification of direct in vivo targets for transcription factors. This approach involves formaldehyde fixation of cells, immunoprecipitation of crosslinked chromatin DNA fragments, and detection of enriched transcription factor binding sites with DNA microarray technologies. The GWLA technique has several distinct advantages over existing ones: First, the method directly examines the in vivo protein-DNA interactions throughout the genome, and can reveal functions of a transcription factor under both normal and diseased states. Second, this unbiased approach : does not require prior knowledge of a transcription factor's function, therefore can uncover its never biological properties. Third, the method has broad applications and can also be applied to discovery of DNA methylation patterns or mapping of other functional elements in the genome relevant to tumorigenesis. Through extensive preliminary experiments, we have verified the utility and exquisite sensitivity of this method with many transcription factors in both yeast and human cells. In the R21 phase studies, we will develop quantitative measures to assess the robustness and reliability of this method. In addition, we will demonstrate that this method can be used to map transcription factor binding sites in mouse genome, in the R33 phase, we will further develop and fully implement a GWLA system to identify targets for human and mouse transcription factors. Because a vast majority of known transcription factors bind close to gene promoters, our GWLA system will be focused on examination of promoter occupancy by specific : transcription factors in cells. We will first annotate gene promoters in the human or mouse genome, and then build DNA microarrays to represent these regions. We will also establish a standard protocol for target identification, and validate the performance of our system using a number of cancer-related transcription factors. This system should prove to be a powerful tool in mechanistic studies as welt as cancer diagnosis.